


"""simple docstring"""






import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


_lowerCAmelCase							:    Optional[int]					   =	logging.getLogger(__name__)

_lowerCAmelCase							:    str					   =	"pytorch_model.bin"




@dataclasses.dataclass
class 							A_      :
  lowerCAmelCase__      			=	dataclasses.field(
      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models.'} )
  lowerCAmelCase__      			=	dataclasses.field(
      default=_SCREAMING_SNAKE_CASE     ,	metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co.'}     ,	)




@dataclasses.dataclass
class 							A_      :
  lowerCAmelCase__      			=	dataclasses.field(metadata={'help': 'A csv or a json file containing the training data.'} )
  lowerCAmelCase__      			=	dataclasses.field(metadata={'help': 'A csv or a json file containing the data to predict on.'} )
  lowerCAmelCase__      			=	dataclasses.field(
      default=_SCREAMING_SNAKE_CASE     ,	metadata={'help': 'A csv or a json file containing the validation data.'} )
  lowerCAmelCase__      			=	dataclasses.field(
      default=_SCREAMING_SNAKE_CASE     ,	metadata={'help': 'The name of the task to train on.'}     ,	)
  lowerCAmelCase__      			=	dataclasses.field(
      default=_SCREAMING_SNAKE_CASE     ,	metadata={'help': 'The list of labels for the task.'} )




@dataclasses.dataclass
class 							A_      :
  lowerCAmelCase__      			=	dataclasses.field(
      metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'} )
  lowerCAmelCase__      			=	dataclasses.field(
      default='accuracy'     ,	metadata={'help': 'The evaluation metric used for the task.'} )
  lowerCAmelCase__      			=	dataclasses.field(
      default='no'     ,	metadata={
          'help': 'The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'
      }     ,	)
  lowerCAmelCase__      			=	dataclasses.field(
      default=1_0     ,	metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}     ,	)
  lowerCAmelCase__      			=	dataclasses.field(
      default=0.0     ,	metadata={
          'help': 'How much the specified evaluation metric must improve to satisfy early stopping conditions.'
      }     ,	)
  lowerCAmelCase__      			=	dataclasses.field(
      default=_SCREAMING_SNAKE_CASE     ,	metadata={'help': 'Whether to filter the pseudo-labeled data based on the confidence score.'}     ,	)
  lowerCAmelCase__      			=	dataclasses.field(
      default=_SCREAMING_SNAKE_CASE     ,	metadata={'help': 'Whether to filter the pseudo-labeled data based on the validation performance.'}     ,	)
  lowerCAmelCase__      			=	dataclasses.field(
      default=_SCREAMING_SNAKE_CASE     ,	metadata={'help': 'Whether to fine-tune on labeled data after pseudo training.'}     ,	)
  lowerCAmelCase__      			=	dataclasses.field(
      default=0.0     ,	metadata={'help': 'Confidence threshold for pseudo-labeled data filtering.'}     ,	)
  lowerCAmelCase__      			=	dataclasses.field(
      default=1_0_0     ,	metadata={'help': 'Number of evaluation calls with no improvement after which training will be stopped.'}     ,	)
  lowerCAmelCase__      			=	dataclasses.field(
      default=_SCREAMING_SNAKE_CASE     ,	metadata={'help': 'Random seed for initialization.'}     ,	)







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase       )    ->     Union[str, Any]:
   '''simple docstring'''





   _lowerCamelCase   :				Dict							=     datasets.concatenate_datasets([infer_input, infer_output]			,		axis=1       )

   if args.do_filter_by_confidence:
      _lowerCamelCase   :				Union[str, Any]							=     dataset.filter(lambda _lowerCamelCase       : example["probability"] > args.confidence_threshold       )

   if args.do_filter_by_val_performance:
      assert eval_result >= 0.0 and eval_result <= 1.0
      _lowerCamelCase   :				Optional[int]							=     int(eval_result * len(lowerCamelCase_       )       )
      print(lowerCamelCase_       )
      _lowerCamelCase   :				Any							=     dataset.sort("probability"			,		reverse=lowerCamelCase_       )
      _lowerCamelCase   :				Any							=     dataset.select(range(lowerCamelCase_       )       )

   _lowerCamelCase   :				Dict							=     dataset.remove_columns(["label", "probability"]       )
   _lowerCamelCase   :				List[str]							=     dataset.rename_column("prediction"			,		"label"       )
   _lowerCamelCase   :				Tuple							=     dataset.map(lambda _lowerCamelCase       : {"label": idalabel[example["label"]]}       )
   _lowerCamelCase   :				Dict							=     dataset.shuffle(seed=args.seed       )

   _lowerCamelCase   :				str							=     os.path.join(lowerCamelCase_			,		F"""train_pseudo.{args.data_file_extension}"""       )
   if args.data_file_extension == "csv":
      dataset.to_csv(lowerCamelCase_			,		index=lowerCamelCase_       )
   else:
      dataset.to_json(lowerCamelCase_       )







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		_lowerCamelCase			,		**_lowerCamelCase       )    ->     Optional[int]:
   '''simple docstring'''





   _lowerCamelCase   :				Optional[Any]							=     Accelerator()
   # Make one log on every process with the configuration for debugging.
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"			,		datefmt="%m/%d/%Y %H:%M:%S"			,		level=logging.INFO			,		)
   logger.info(accelerator.state       )

   # Setup logging, we only want one process per machine to log things on the
   # screen. accelerator.is_local_main_process is only True for one process per
   # machine.
   logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR       )

   if accelerator.is_local_main_process:
      datasets.utils.logging.set_verbosity_warning()
      transformers.utils.logging.set_verbosity_info()
   else:
      datasets.utils.logging.set_verbosity_error()
      transformers.utils.logging.set_verbosity_error()

   _lowerCamelCase   :				List[str]							=     STModelArguments(model_name_or_path=lowerCamelCase_       )
   _lowerCamelCase   :				List[Any]							=     STDataArguments(train_file=lowerCamelCase_			,		infer_file=lowerCamelCase_       )
   _lowerCamelCase   :				int							=     STTrainingArguments(output_dir=lowerCamelCase_       )
   _lowerCamelCase   :				Any							=     argparse.Namespace()

   for arg_class in (model_args, data_args, training_args):
      for key, value in vars(lowerCamelCase_       ).items():
         setattr(lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_       )

   for key, value in kwargs.items():
      if hasattr(lowerCamelCase_			,		lowerCamelCase_       ):
         setattr(lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_       )

    # Sanity checks
   _lowerCamelCase   :				Tuple							=     {}
   _lowerCamelCase   :				str							=     None

   # You need to provide the training data and the data to predict on
   assert args.train_file is not None
   assert args.infer_file is not None
   _lowerCamelCase   :				Dict							=     args.train_file
   _lowerCamelCase   :				Any							=     args.infer_file

   if args.evaluation_strategy != IntervalStrategy.NO.value:
      assert args.eval_file is not None
      _lowerCamelCase   :				Optional[int]							=     args.eval_file

   for key in data_files:
      _lowerCamelCase   :				str							=     data_files[key].split("."       )[-1]
      assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
      if args.data_file_extension is None:
         _lowerCamelCase   :				List[Any]							=     extension
      else:
         assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""

   assert (
       args.eval_metric in datasets.list_metrics()
   ), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

   # If passed along, set the training seed now.
   if args.seed is not None:
      set_seed(args.seed       )

   logger.info("Creating the initial data directory for self-training..."       )
   _lowerCamelCase   :				Any							=     F"""{args.output_dir}/self-train_iter-{{}}""".format
   _lowerCamelCase   :				Tuple							=     data_dir_format(0       )

   if accelerator.is_main_process:
      if args.output_dir is not None:
         os.makedirs(args.output_dir			,		exist_ok=lowerCamelCase_       )
         os.makedirs(lowerCamelCase_			,		exist_ok=lowerCamelCase_       )
   accelerator.wait_for_everyone()

   _lowerCamelCase   :				str							=     None
   _lowerCamelCase   :				List[str]							=     None
   _lowerCamelCase   :				List[str]							=     0
   _lowerCamelCase   :				List[Any]							=     False
   # Show the progress bar
   _lowerCamelCase   :				Any							=     tqdm(range(args.max_selftrain_iterations       )			,		disable=not accelerator.is_local_main_process       )

   # Self-train
   for iteration in range(0			,		int(args.max_selftrain_iterations       )       ):
      _lowerCamelCase   :				Union[str, Any]							=     data_dir_format(lowerCamelCase_       )
      assert os.path.exists(lowerCamelCase_       )

      # Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
      # iteration > 0
      _lowerCamelCase   :				str							=     os.path.join(lowerCamelCase_			,		"stage-1"       )
      _lowerCamelCase   :				Optional[Any]							=     {
          '''accelerator''': accelerator,
          '''model_name_or_path''': args.model_name_or_path,
          '''cache_dir''': args.cache_dir,
          '''do_train''': True,
          '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
          '''do_eval''': True if args.eval_file is not None else False,
          '''eval_file''': data_files['''eval'''],
          '''do_predict''': True,
          '''infer_file''': data_files['''infer'''],
          '''task_name''': args.task_name,
          '''label_list''': args.label_list,
          '''output_dir''': current_output_dir,
          '''eval_metric''': args.eval_metric,
          '''evaluation_strategy''': args.evaluation_strategy,
          '''early_stopping_patience''': args.early_stopping_patience,
          '''early_stopping_threshold''': args.early_stopping_threshold,
          '''seed''': args.seed,
      }
      # Add additional training arguments
      for key, value in kwargs.items():
         if key not in arguments_dict and not hasattr(lowerCamelCase_			,		lowerCamelCase_       ):
            arguments_dict.update({key: value}       )

      _lowerCamelCase   :				str							=     os.path.join(lowerCamelCase_			,		"best-checkpoint"			,		lowerCamelCase_       )
      if os.path.exists(lowerCamelCase_       ):
         logger.info(
             "Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1."			,		lowerCamelCase_			,		lowerCamelCase_			,		)
      else:
         logger.info("***** Running self-training: iteration: %d, stage: 1 *****"			,		lowerCamelCase_       )
         finetune(**lowerCamelCase_       )
         accelerator.wait_for_everyone()
         assert os.path.exists(lowerCamelCase_       )
         logger.info("Self-training job completed: iteration: %d, stage: 1."			,		lowerCamelCase_       )

      if iteration > 0 and args.finetune_on_labeled_data:
         # Stage 2 (optional): fine-tuning on the original labeled data
         _lowerCamelCase   :				Any							=     os.path.join(lowerCamelCase_			,		"best-checkpoint"       )
         _lowerCamelCase   :				int							=     os.path.join(lowerCamelCase_			,		"stage-2"       )
         # Update arguments_dict
         _lowerCamelCase   :				int							=     model_path
         _lowerCamelCase   :				Optional[Any]							=     data_files['''train''']
         _lowerCamelCase   :				Tuple							=     current_output_dir

         _lowerCamelCase   :				List[str]							=     os.path.join(lowerCamelCase_			,		"best-checkpoint"			,		lowerCamelCase_       )
         if os.path.exists(lowerCamelCase_       ):
            logger.info(
                "Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2."			,		lowerCamelCase_			,		lowerCamelCase_			,		)
         else:
            logger.info("***** Running self-training: iteration: %d, stage: 2 *****"			,		lowerCamelCase_       )
            finetune(**lowerCamelCase_       )
            accelerator.wait_for_everyone()
            assert os.path.exists(lowerCamelCase_       )
            logger.info("Self-training job completed: iteration: %d, stage: 2."			,		lowerCamelCase_       )

      _lowerCamelCase   :				List[Any]							=     iteration
      _lowerCamelCase   :				Optional[Any]							=     data_dir_format(iteration + 1       )

      _lowerCamelCase   :				Tuple							=     AutoConfig.from_pretrained(os.path.join(lowerCamelCase_			,		"best-checkpoint"       )       )
      _lowerCamelCase   :				List[Any]							=     config.idalabel
      _lowerCamelCase   :				Optional[int]							=     os.path.join(lowerCamelCase_			,		"eval_results_best-checkpoint.json"       )
      _lowerCamelCase   :				Union[str, Any]							=     os.path.join(lowerCamelCase_			,		"test_results_best-checkpoint.json"       )
      assert os.path.exists(lowerCamelCase_       )

      with open(lowerCamelCase_			,		"r"       ) as f:
         _lowerCamelCase   :				Optional[int]							=     float(json.load(lowerCamelCase_       )[args.eval_metric]       )
      _lowerCamelCase   :				Any							=     os.path.join(lowerCamelCase_			,		"infer_output_best-checkpoint.csv"       )
      assert os.path.exists(lowerCamelCase_       )
      # Loading the dataset from local csv or json files.
      _lowerCamelCase   :				str							=     load_dataset(args.data_file_extension			,		data_files={"data": data_files["infer"]}       )['''data''']
      _lowerCamelCase   :				Tuple							=     load_dataset("csv"			,		data_files={"data": infer_output_file}       )['''data''']

      if accelerator.is_main_process:
         os.makedirs(lowerCamelCase_			,		exist_ok=lowerCamelCase_       )
         shutil.copy(lowerCamelCase_			,		os.path.join(lowerCamelCase_			,		F"""eval_results_iter-{iteration}.json"""       )       )
         if os.path.exists(lowerCamelCase_       ):
            shutil.copy(lowerCamelCase_			,		os.path.join(lowerCamelCase_			,		F"""test_results_iter-{iteration}.json"""       )       )
         create_pseudo_labeled_data(lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_			,		lowerCamelCase_       )
      accelerator.wait_for_everyone()

      _lowerCamelCase   :				Optional[int]							=     os.path.join(lowerCamelCase_			,		F"""train_pseudo.{args.data_file_extension}"""       )

      if args.evaluation_strategy != IntervalStrategy.NO.value:
         _lowerCamelCase   :				Any							=     eval_result

         if best_iteration is None:
            _lowerCamelCase   :				int							=     new_iteration
            _lowerCamelCase   :				Optional[Any]							=     new_eval_result
         else:
            if new_eval_result - best_eval_result > args.early_stopping_threshold:
               _lowerCamelCase   :				Optional[int]							=     new_iteration
               _lowerCamelCase   :				Any							=     new_eval_result
               _lowerCamelCase   :				Any							=     0
            else:
               if new_eval_result == best_eval_result:
                  _lowerCamelCase   :				Any							=     new_iteration
                  _lowerCamelCase   :				Any							=     new_eval_result
               early_stopping_patience_counter += 1

            if early_stopping_patience_counter >= args.early_stopping_patience:
               _lowerCamelCase   :				int							=     True

      progress_bar.update(1       )

      if should_training_stop:
         break

   if best_iteration is not None:
      # Save the best iteration
      logger.info("Best iteration: %d"			,		lowerCamelCase_       )
      logger.info("Best evaluation result: %s = %f"			,		args.eval_metric			,		lowerCamelCase_       )
      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
         shutil.copy(
             os.path.join(lowerCamelCase_			,		F"""eval_results_iter-{iteration}.json"""       )			,		os.path.join(lowerCamelCase_			,		"eval_results_best-iteration.json"       )			,		)
   else:
      # Assume that the last iteration is the best
      logger.info("Best iteration: %d"			,		args.max_selftrain_iterations - 1       )
      logger.info("Best evaluation result: %s = %f"			,		args.eval_metric			,		lowerCamelCase_       )
      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
         shutil.copy(
             os.path.join(lowerCamelCase_			,		F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""       )			,		os.path.join(lowerCamelCase_			,		"eval_results_best-iteration.json"       )			,		)


import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowercase			:				List[str]   =     logging.get_logger(__name__)






def        snake_case__		(    lowerCamelCase_=None     ,				lowerCamelCase_=None		):
							return field(default_factory=lambda: default     ,				metadata=lowerCamelCase_		)







@dataclass
class 					__lowercase  :





							"""simple docstring"""

							UpperCAmelCase_						:		List[str]								=	list_field(
							    default=[]		,       metadata={
							        '''help''': (
							            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
							            ''' of all available models'''
							        )
							    }		,       )

							UpperCAmelCase_						:		List[int]								=	list_field(
							    default=[8]		,       metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}				)

							UpperCAmelCase_						:		List[int]								=	list_field(
							    default=[8, 32, 1_28, 5_12]		,       metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}		,       )

							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Use FP16 to accelerate inference.'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Benchmark training of model'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Verbose memory tracing'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}		,       )
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
							    }		,       )
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Trace memory line by line'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Save result to a CSV file'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Save all print statements in a log file'''}				)
							UpperCAmelCase_						:		bool								=	field(default=_SCREAMING_SNAKE_CASE		,       metadata={'''help''': '''Whether to print environment information'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
							            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
							            ''' for debugging / testing and on TPU.'''
							        )
							    }		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'inference_time_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'inference_memory_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'train_time_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'train_memory_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'env_info_{round(time()				)}.csv'		,       metadata={'''help''': '''CSV filename used if saving environment information.'''}		,       )
							UpperCAmelCase_						:		str								=	field(
							    default=F'log_{round(time()				)}.csv'		,       metadata={'''help''': '''Log filename used if print statements are saved in log.'''}		,       )
							UpperCAmelCase_						:		int								=	field(default=3		,       metadata={'''help''': '''Times an experiment will be run.'''}				)
							UpperCAmelCase_						:		bool								=	field(
							    default=_SCREAMING_SNAKE_CASE		,       metadata={
							        '''help''': (
							            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
							            ''' model weights.'''
							        )
							    }		,       )




							def       snake_case			(					self						)		->       Union[str, Any]:
														warnings.warn(
														    f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
														    ''' are deprecated in general and it is advised to use external Benchmarking libraries '''
														    ''' to benchmark Transformer models.'''    ,						__UpperCAmelCase    ,						)




							def       snake_case			(					self						)		->       Union[str, Any]:
														return json.dumps(dataclasses.asdict(self						)    ,						indent=2						)




							@property
							def       snake_case			(					self						)		->       List[str]:
														if len(self.models						) <= 0:
																					raise ValueError(
																					    '''Please make sure you provide at least one model name / model identifier, *e.g.* `--models'''
																					    ''' bert-base-cased` or `args.models = [\'bert-base-cased\'].'''						)
														return self.models






							@property
							def       snake_case			(					self						)		->       int:
														if not self.multi_process:
																					return False
														elif self.is_tpu:
																					logger.info('''Multiprocessing is currently not possible on TPU.'''						)
																					return False
														else:
																					return True






